Networks are useful for representing phenomena in a broad range of domains [1]}, [2]}. Although their ability to represent complexity can be a virtue, in some cases (e.g., computationally intensive analysis, presence of noise, visualization) it is useful to simplify a network and focus instead on its backbone. Given a complex network \(\mathbf {N}\) , which may be weighted or unweighted, its backbone \(\mathbf {N^{\prime }}\)  is a sparse and unweighted subgraph that aims to preserve or reveal important structural features. Many backbone methods have been proposed for extracting a backbone \(\mathbf {N^{\prime }}\)  from a network \(\mathbf {N}\) , with different methods designed for different types of networks, or to preserve different types of structural features. However, applying these methods in practice has been challenging because they were either not implemented or implemented in many different software languages.
